Biostats test 2

Question 1

deviation score

Answer 1

xi-x bar

Question 2

sample variance

Answer 2

sum of all squared deviation scores divided by n-1. (s squared)

Question 3

purpose of ANOVA

Answer 3

splitting total variance in two parts; one pertaining to differences between groups and one pertaining to differences within groups.

Question 4

F

Answer 4

mean squares between groups (explained varianace; effect)/mean squares within groups (unexplained variance; error). if F takes on a sufficiently large value, we can reject H0 (still need to compare p versus alpha.

Question 5

another definition of variance

Answer 5

total sum of squares divided by n-1

Question 6

total sum of squares

Answer 6

basically the top half of variance; sum of squared deviation scores between all n data points (summed over all groups), relative to the grand mean (mean of all those n data points). between groups sum of squares + within groups sum of squares

Question 7

within groups sum of squares

Answer 7

sum of squared deviation scores between each of k group’s individual values relative to that k group’s mean, summed over all groups

Question 8

between groups sum of squares

Answer 8

sum of squared deviation scores between each between of k group means relative to grand mean

Question 9

degrees of freedom

Answer 9

number of values that are free to vary given a boundary condition

Question 10

how many degrees of freedom does variance have

Answer 10

n-1 because given a mean value, one degree of freedom is lost to compute the variance around the mean

Question 11

df total sum of squares

Answer 11

n-1 because one mean is constrained by the others

Question 12

df between groups sum of squares

Answer 12

k-1, one mean is constrained by the others

Question 13

df within groups sum of squares

Answer 13

n-k (one degree of freedom is used up in calculating each groups mean, so since there are k groups ,we lost k degrees of freedom, one for each group mean)

Question 14

mean squares

Answer 14

computed on basis of different sums of squares by dividing the sums of squares by their degrees of freedom; so mean squares for total squares is actually variance!

Question 15

one way ANOVA

Answer 15

one factor, eg drug dose on mean reaction time

Question 16

Assumptions for one way anova

Answer 16

within group variability is unexplained; considered error variance. we check for unequal precision using Levene’s

Question 18

Levene’s test for homogeneity of error variance

Answer 18

H0: all of the group’s distribution and errors differ in approximately the same way, regardless of the mean for each group.

Question 19

Factorial anova

Answer 19

two factors or more, can each have different levels. for example three drug dose levels and two biological sexes. 3x2 = 6 mean values. then we can also have interactions.

Question 20

effect modification

Answer 20

effect of one IV on DV is different for levels of another IV

Question 20

mushrooming

Answer 20

when adding factors to design multiplies effect modification terms

Question 21

independent samples t-test

Answer 21

two independent groups, compare their means; like one way anova with just two levels

Question 22

repeated measures t dest

Answer 22

matched, dependent samples t-test: comparison of paired values

Question 23

one-sample t-test

Answer 23

on sample mean compared to a given, fixed value

Question 24

MANOVA

Answer 24

multivariate analysis of variance - use when we wish to look at mean difference across several dependent variables because they are believed to be meaningfully related (eg multiple factors of a construct such as biodiversity, or health)

Question 25

Hierarchy of when to use MANOVA and ANOVA

Answer 25

if we want to assess the effect of IVs on the collective body of DVs, we first use MANOVA. If there is no effect, we do not need to proceed. If there is an effect, we conduct ANOVA to find out for which DVs the IVs have an effect. For significant ANOVAs, we may want to compare the effect of factor levels.

Question 26

When do we use repeated measures ANOVA

Answer 26

when we are comparing more than 2 means and the subjects are measured either at different time instants or in several conditions. time and condition are within-subject factors that create dependencies in the data.

Question 27

difference between factors and levels

Answer 27

factor: drug dose. levels: low, medium, high

Question 28

Repeated measures ANOVA -mixed

Answer 28

if both within-subject and between-subject factors are included in one model, eg comparing the effects of treatment type (BS, 2 levels) over 4 time (WS, 4 levels)

Question 29

Sphericity assumption

Answer 29

difference scores between levels of a within-subjects factor have the same variance for the comparison of any two levels

Question 30

Mauchly’s test

Answer 30

tests sphericity (equality of variance of difference scores between any two groups). If Mauchly’s W is significant, sphericity cannot be assumed. If sphericity assumptions is violated, df’s must be corrected (made smaller, more conservative) by some kind of scheme.

Question 31

MANOVA for repeated measures

Answer 31

the sets of values measured at different time instants are considered different (but correlated) dependent variables. since we cannot look at difference scores when comparing scores on different variables, there is no more sphericity that could be violated.

Question 32

What an interaction tells us in a two-way ANOVA (or repeated measures ANOVA with between-subjects factors)

Answer 32

an interaction effect occurs when the effect of one independent variable (treatment) on the dependent variable (CO2 uptake) is not consistent across the levels of another independent variable (population).